Face recognition is still challenging due to the large variations of facial appearance, caused by lighting, partial occlusions, head pose, etc. The feature extraction is a key step for face recognition. In order to improve the recognition rate of face recognition,we introduce a novel feature extraction technique for face recognition, which is a combination of compressed sensing and spatial pyramid model method. The scale invariant feature transform is first used to be a feature extractor to obtain facial features.Then by using sparse coding in the randomly generated dictionary, dimensionalities of those features are reduced. After the spatial pyramid is used to be a feature extractor to obtain different spatial scales, the max pool is used to integrate the features. Finally, the kernel sparse representation classifier is proposed to classify the features to complete the face recognition. The experimental results based on the Extended Yale B, AR and CMU PIE databases demonstrate that the method has a strong rustness in the illumination, pose and disguise variation with a faster running speed.